Next generation wireless systems are expected to support demanding applications such as real-time services. Real-time services over a shared downlink channel require not only a real-time scheduler but also efficient admission control, commonly referred to as as call admission control (CAC). (Note that, as used herein, the term “call” is not limited to a telephone call or any specific media type.) Admission control decides whether the system has the resources to accommodate a newly arrived user or call, such as a streaming video or VoIP request, without sacrificing the quality of service (QoS) of existing users. The CAC function is particularly important to systems such as cellular wireless systems in which users are continuously entering and leaving cellular coverage while meeting requirements for guaranteed services for each user. Admission control is an indispensable part of the system and one of the key components driving QoS in such systems. Admission control directly decides how efficient the system could be, i.e., how many real-time users the wireless system can support. As such, admission control will have a significant impact on users' satisfaction and thus system revenues.
There is a large selection of underlying, packet-level scheduling approaches, including the proportional fairness (PF), modified largest weighted delay first (MLWDF), and exponential rule (Exp-Rule) algorithms. Besides MLWDF and Exp-Rule, however, few real-time scheduling algorithms are designed specifically for shared-channel cellular systems. See M. Andrews et al., “Providing Quality of Service over a Shared Wireless Link,” IEEE Commun. Mag., pp. 15054 (February 2001); S. Shakkottai and A. Stolyar, “Scheduling Algorithms for Mixture of Real-time and NonReal-time Data in HDR,” in Proceedings 17th Int. Teletraffic Congress (ITC17) (September 2001). The MLWDF and Exp-Rule algorithms are both a weighted versions of PF, where per-user weight equals the head-of-line (HOL) packet delay. While they have been shown to be “throughput optimal”, they are not necessarily delay optimal.
In wired networking systems, the earliest due date (EDD) and the shortest time to extinction (STE) algorithms have been shown to be optimal to minimize the mean queuing delay and the deadline-violated packet losses, respectively. On the other hand, FIFO queuing as the simplest scheduling algorithm is known to minimize the maximal queuing delay. For the simple case that all arrived packets have the same expiration time Ds, the EDD algorithm and FIFO queuing become equivalent. These approaches, however, have low efficiency in the cellular environment due to their lack of channel awareness.
With regard to admission control, in contrast to the numerous admission control techniques applicable to legacy circuit-switched cellular systems, where each user has a power-controlled dedicated channel, there have been few techniques for dealing with flow or user-level admission control in cellular downlink shared channel systems. At the flow level, only a few admission control techniques have been proposed for such systems. See T. Bonald and A. Proutiere, “Wireless Downlink Data Channels: User Performance and Cell Dimensioning,” Proceedings of ACM MOBICOM, pp. 33952 (September 2003); S. Das, H. Viswanathan, G. Rittenhouse, “Dynamic Load Balancing Through Coordinated Scheduling in Packet Data Systems,” IEEE Proceedings of INFOCOM, pp. 78696 (April 2003).
The aforementioned approaches, however, suffer from inefficiencies due to their neglect of multi-user diversity gain at the scheduler (or packet) level. To take advantage of multi-user diversity gain, a sender should choose the receiver with the best channel quality in order to maximize the system spectral efficiency. On the other hand, there are several channel-dependent, opportunistic scheduling algorithms to exploit such gain with or without delay awareness. These scheduling approaches, however, assume a fixed number of users or a system of static traffic loads.
Particularly in cellular systems, there are significant challenges in accurately capturing the load of each user and of the whole system given the location-dependent and user-specific channel quality and in characterizing the per-user QoS and wireless resource given an opportunistic scheduler.